
and solving the optimization problem (REF ) would give us an optimal shared representation \(x\) . A critical issue with this approach is that \(f\)  and \(g\)  are measuring the average loss over all tasks, which can lead to a solution with good average performance but poor worst-case performance. This is problematic in safety-critical scenarios such as few-shot facial recognition. If each task represents a different population demographic, a representation \(x\)  with a poor worst-case loss but good average-case loss can be very biased towards a certain demographic. [1]} highlights this problem in the meta-learning setup, showing that popular algorithms such as MAML [2]} indeed show poor worst-case performance. To mitigate this problem, they propose a task-robust MAML objective to minimize the worst-case loss rather than the average-case loss.
